Zachary Depp, Halit Bugra Tulay, C. Emre Koksal (The Ohio State University)

The traditional vehicular roll-jam attack is an effective means to gain access to the target vehicle by jamming and recording key fob inputs from a victim. However, it requires specific knowledge of the attack surface, and delicate tuning of software-defined radio parameters. We have developed an enhanced version of the roll-jam attack that uses a known noise signal for jamming, in contrast to the additive white Gaussian noise that is typically used in the attack. Using a known noise signal allows for less strict tuning of the software-defined radios used in the attack, and allows for digital noise removal of the recorded input to enhance the replay attack.

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Investigating the Impact of Evasion Attacks Against Automotive Intrusion...

Paolo Cerracchio, Stefano Longari, Michele Carminati, Stefano Zanero (Politecnico di Milano)

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Improving In-vehicle Networks Intrusion Detection Using On-Device Transfer Learning

Sampath Rajapaksha (Robert Gordon University), Harsha Kalutarage (Robert Gordon University), M.Omar Al-Kadri (Birmingham City University), Andrei Petrovski (Robert Gordon University), Garikayi Madzudzo (Horiba Mira Ltd)

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AVMON: Securing Autonomous Vehicles by Learning Control Invariants and...

Ahmed Abdo, Sakib Md Bin Malek, Xuanpeng Zhao, Nael Abu-Ghazaleh (University of California, Riverside)

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